Reconstructing open surfaces from multi-view images is vital in digitalizing complex objects in daily life. A widely used strategy is to learn unsigned distance functions (UDFs) by checking if their appearance conforms to the image observations through neural rendering. However, it is still hard to learn continuous and implicit UDF representations through 3D Gaussians splatting (3DGS) due to the discrete and explicit scene representation, i.e., 3D Gaussians. To resolve this issue, we propose a novel approach to bridge the gap between 3D Gaussians and UDFs. Our key idea is to overfit thin and flat 2D Gaussian planes on surfaces, and then, leverage the self-supervision and gradient-based inference to supervise unsigned distances in both near and far area to surfaces. To this end, we introduce novel constraints and strategies to constrain the learning of 2D Gaussians to pursue more stable optimization and more reliable self-supervision, addressing the challenges brought by complicated gradient field on or near the zero level set of UDFs. We report numerical and visual comparisons with the state-of-the-art on widely used benchmarks and real data to show our advantages in terms of accuracy, efficiency, completeness, and sharpness of reconstructed open surfaces with boundaries. Project page: https://lisj575.github.io/GaussianUDF/

With the increasing complexity of rail transit systems, traditional signal priority strategies, which rely on fixed rules, often fail to adapt to dynamic traffic conditions and lack flexibility and scalability. This study introduces a knowledge-driven approach leveraging a deep Q-network (DQN) to optimize signal priority strategies in rail transit systems. By constructing a traffic state representation model, dynamic features are captured and input into a neural network (NN), creating a high-dimensional state space for decision-making. The DQN framework integrates an experience replay mechanism and target network updates to enhance learning stability, ensuring real-time adaptation and continuous optimization. A reward function is defined to balance train delay minimization and road-traffic coordination, achieving system-wide performance improvement. Experimental results demonstrate that, under traffic flow conditions of 300 vehicles/hour, the proposed DQN strategy reduces train delay by 50% and increases road traffic flow by 16.67%.

Point cloud upsampling aims to generate dense and uniformly distributed point sets from a sparse point cloud, which plays a critical role in 3D computer vision. Previous methods typically split a sparse point cloud into several local patches, upsample patch points, and merge all upsampled patches. However, these methods often produce holes, outliers or non-uniformity due to the splitting and merging process which does not maintain consistency among local patches.To address these issues, we propose a novel approach that learns an unsigned distance field guided by local priors for point cloud upsampling. Specifically, we train a local distance indicator (LDI) that predicts the unsigned distance from a query point to a local implicit surface. Utilizing the learned LDI, we learn an unsigned distance field to represent the sparse point cloud with patch consistency. At inference time, we randomly sample queries around the sparse point cloud, and project these query points onto the zero-level set of the learned implicit field to generate a dense point cloud. We justify that the implicit field is naturally continuous, which inherently enables the application of arbitrary-scale upsampling without necessarily retraining for various scales. We conduct comprehensive experiments on both synthetic data and real scans, and report state-of-the-art results under widely used benchmarks. Project page: https://lisj575.github.io/APU-LDI

Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions from point clouds, which are limited to reconstructing closed surfaces. Some other methods tried to represent open surfaces using unsigned distance functions (UDF) which are learned from ground truth distances. However, the learned UDF is hard to provide smooth distance fields due to the discontinuous character of point clouds. In this paper, we propose CAP-UDF, a novel method to learn consistency-aware UDF from raw point clouds. We achieve this by learning to move queries onto the surface with a field consistency constraint, where we also enable to progressively estimate a more accurate surface. Specifically, we train a neural network to gradually infer the relationship between queries and the approximated surface by searching for the moving target of queries in a dynamic way. Meanwhile, we introduce a polygonization algorithm to extract surfaces using the gradients of the learned UDF. We conduct comprehensive experiments in surface reconstruction for point clouds, real scans or depth maps, and further explore our performance in unsupervised point normal estimation, which demonstrate non-trivial improvements of CAP-UDF over the state-of-the-art methods.

Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the early stage of the disease. In this paper, a Parkinson's auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning is proposed for the Parkinson's diagnosis. The diagnosis system uses ResNet50 to achieve feature extraction and Parkinson's classification, mainly including speech signal processing part, algorithm improvement part based on Artificial Bee Colony algorithm (ABC) and optimizing the hyperparameters of ResNet50 part. The improved algorithm is called Gbest Dimension Artificial Bee Colony algorithm (GDABC), proposing "Range pruning strategy" which aims at narrowing the scope of search and "Dimension adjustment strategy" which is to adjust gbest dimension by dimension. The accuracy of the diagnosis system in the verification set of Mobile Device Voice Recordings at King's College London (MDVR-CKL) dataset can reach more than 96%. Compared with current Parkinson's sound diagnosis methods and other optimization algorithms, our auxiliary diagnosis system shows better classification performance on the dataset within limited time and resources.

Normal estimation for unstructured point clouds is an important task in 3D computer vision. Current methods achieve encouraging results by mapping local patches to normal vectors or learning local surface fitting using neural networks. However, these methods are not generalized well to unseen scenarios and are sensitive to parameter settings. To resolve these issues, we propose an implicit function to learn an angle field around the normal of each point in the spherical coordinate system, which is dubbed as Neural Angle Fields (NeAF). Instead of directly predicting the normal of an input point, we predict the angle offset between the ground truth normal and a randomly sampled query normal. This strategy pushes the network to observe more diverse samples, which leads to higher prediction accuracy in a more robust manner. To predict normals from the learned angle fields at inference time, we randomly sample query vectors in a unit spherical space and take the vectors with minimal angle values as the predicted normals. To further leverage the prior learned by NeAF, we propose to refine the predicted normal vectors by minimizing the angle offsets. The experimental results with synthetic data and real scans show significant improvements over the state-of-the-art under widely used benchmarks. Project page: https://lisj575.github.io/NeAF/.

Latest methods represent shapes with open surfaces using unsigned distance functions (UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the gradients around the zero level set of the UDF. However, the differential networks struggle from learning the zero level set where the UDF is not differentiable, which leads to large errors on unsigned distances and gradients around the zero level set, resulting in highly fragmented and discontinuous surfaces. To resolve this problem, we propose to learn a more continuous zero level set in UDFs with level set projections. Our insight is to guide the learning of zero level set using the rest non-zero level sets via a projection procedure. Our idea is inspired from the observations that the non-zero level sets are much smoother and more continuous than the zero level set. We pull the non-zero level sets onto the zero level set with gradient constraints which align gradients over different level sets and correct unsigned distance errors on the zero level set, leading to a smoother and more continuous unsigned distance field. We conduct comprehensive experiments in surface reconstruction for point clouds, real scans or depth maps, and further explore the performance in unsupervised point cloud upsampling and unsupervised point normal estimation with the learned UDF, which demonstrate our non-trivial improvements over the state-of-the-art methods. Code is available at https://github.com/junshengzhou/LevelSetUDF.

Abstract Robot deburring is an effective method for improving the surface quality of the high‐voltage copper contact. The first step of robot deburring is to acquire the burr images. We propose a new burr mathematical model and build a real burr image dataset for burr image denoising. In order to improve burr image denoising effects of the high‐voltage copper contact, this study proposes an online burr image denoising algorithm, that is, block cosparsity overcomplete learning transform algorithm (BCOLTA). The penalty term and the condition number are affected by the burr parameter. The clustering and transform alternate minimisation algorithms are adopted to achieve lower computational cost and better denoising effect. In addition, BCOLTA also has a good adaptibility to inherent noise images, especially in Gaussian noise. Compared with other traditional and deep learning algorithms by no reference and full reference image quality assessment methods, BCOLTA has state‐of‐the‐art denoising effects and computational complexity on dealing with burr images. This research will play an important role in the intelligent manufacturing field.

To improve the performance of the high‐voltage copper contact burr image segmentation, a block cosparsity overcomplete learning transform image segmentation algorithm based on burr model is proposed in this study. In this study, k ‐means clustering method is used to initialise the clustering results; the authors found the algorithm is very effective for burr image processing in production process and the sparse overcomplete transform matrix is initialised by discrete cosine transform. The algorithm is expressed by a set of transforms. When the set of transforms is fixed, the penalty is corresponding to the condition number. A new burr model is proposed in this study. The parameters of the burr are the factors on infection of the sparse‐level constant and the regularisation coefficient of the block cosparsity overcomplete learning transform algorithm. The algorithm divides all pixels into several groups. To evaluate the performance of the model, a large number of experiments have been carried out, and three image segmentation evaluation criterions have been used to evaluate the effectiveness of the algorithm. Experimental results show that this method is excellent in retaining weak edge information and avoiding the influence of three‐dimensional structure compared with other algorithms.

The burrs on high‐voltage copper contact leads to point discharge and device damage. Since the high‐voltage copper contact has different machining batch and the contour various which the machine tool remove is not economic and robot usually is used to remove the burrs of high‐voltage copper contact. The first step for robot deburring is to identify burrs. In order to improve the performance of copper contact burr video denoising, this article presents online burr video denoising sparsifying transforms algorithm, which defined two alternative values between the optimal sparse signal and transform learning dictionary, simultaneously, calculated the mean of peak signal‐to‐noise ratio, the mean of execution time, the STD (STandard Deviation), and the VAR (VARiance), accordingly presented an burr video denoising algorithm and compared with state‐of‐the‐art video denoising algorithms. The experiment results show that compared with traditional methods, the burr video denoising algorithm has higher denoising precision, faster denoising speed, and stronger high‐noise‐level processing capacity, and so on. The numeric experiments show that the proposed approach has higher peak signal‐to‐noise ratio and less computation complexity than the existing video denoising methods.

The partner selection problem with a due date constraint in virtual enterprises is proved to be NPcompleteness problem. So it cannot have any polynomial time solution algorithm at present. Nonlinear integer programming model for the problem is established. The objective function and constraint function of the model have monotonicity properties. Based on the above observations, a Branch-and-Bound algorithm is constructed to solve the problem. Numerical experiments show that the algorithm is effective.